The use of reinforcement learning for efficient robot programming has proven significant potential in research. Particularly in combination with advanced simulations, even complex assembly processes including variation and tolerances can be trained with little effort. However, reliable information about the system’s current success state is needed to reward promising actions for training the reinforcement learning agent. While this success information is readily available in simulation or traditionally retrieved with rule-based approaches, a solution approach to infer the success state from available observation data would highly increase the robustness of the reward information and the subsequent transfer to reality. In this paper, we present a deep learning approach to learn the success criteria using the assembly benchmark process of peg-in-hole.

Increasingly volatile markets challenge companies and demand flexible production systems that can be quickly adapted to new conditions. Machine Learning has proven to show significant potential in supporting the human operator during the time-consuming and complex task of robot pro-gramming by identifying relevant parameters of the underlying robot control program. We present a solution to learn these parameters for contact-rich, force-controlled assembly tasks from a simulation using hardware-independent robot skills. We show that successful learning and real-world execution are possible even under process deviation and tolerances utilizing the designed learning system. We present learning skill param-eters as high-level robot control, evaluation and comparison of extensive simulations, and preliminary experiments on a physical robot test-bed. The developed solution approach is evaluated and discussed using the Peg-in-Hole process, a typical benchmark process in force-controlled assembly.

Programming-by-Demonstration (PbD) or Imitation Learning (IL) provides a powerful approach to program robots intuitively. In industrial settings, these approaches are not commonly deployed since several factors negatively impact their productive use. The most common reasons are safety concerns on various levels. First, industrial robots are only allowed to be operated in direct contact if the operator has sufficient experience. Secondly, many PbD systems do not incorporate force measurements in their model directly. This renders them ineffective for assembly tasks, such as snap- fit connections. In this paper we (a) present an approach to incorporate force measurements into a generative model (b) using only external sensors without relying on measurements from the robot during demonstrating to decouple the teaching process from the robot and (c) show the benefit of explicit force measurement and modeling on a Franka Emika Panda robot by the example of assembling terminal clamps on a DIN rail.

Recent intuitive robot programming approaches operate on task level, enabling programmers to intuitively arrange or compose encapsulating robot capabilities (skills). This paper presents an approach to intuitively create (sub-)skills. General guidelines for assembly process descriptions #2860 provided by the Association of German Engineers VDI are applied to robot programming, in particular addressing assembly applications. The guidelines are exemplarily applied to the constraint-based approach iTaSC (instantaneous Task Specification using Constraints), presenting a procedure to hierarchically combine elementary processes to (sub-)skills. Six elementary processes are identified to be sufficient to implement a wide variety of assembly tasks. An iTaSC implementation was developed and two exemplarily assembly tasks were realized to evaluate the approach.

In recent years, a number of publications described approaches for model-based manipulation skills and their applicability to a variety of robot tasks-be it assembly, industrial robotics in general, or service robotics. These approaches roughly follow the same pattern: They model robot task description based on the Task Frame Formalism, the Task Function Approach, or iTaSC. They model coordination mechanisms in form of statecharts or Petri nets. And almost all models are accompanied by domain-specific languages (DSLs) that facilitate creating applications based on those models. While one advantage of using models is their reusability across applications, how to explicitly model the reuse itself has not been fully addressed by these publications. Our paper contributes to this field of research by investigating how reuse can be explicitly modeled using prototype-based inheritance. We base our model on iTaSC and provide a simple yet effective DSL for populating the model and creating applications. We demonstrate our approach by creating a comprehensive library of skills, and by showing the use, reuse and incremental refinement of skills for diverse industrial assembly applications.